Process engineering involves the design of a wide variety of processing plants and processes carried out therein. Such processes include, but are not limited to, chemical, petrochemical, refining, pharmaceutical, polymer, plastics and other process industries. In process engineering, the use of computer based models to develop and evaluate new processes, design and retrofit plants, and optimize the operation of existing plants is rapidly becoming a standard. At every stage of process design, development and operation, rigorous models generated by process simulation software systems can be used to make better engineering and business decisions.
In a process simulation software system, the performance of a process industry plant in which there is a continuous flow of materials and energy through a network of process units (i.e., equipment such as distillation columns, retaining vessels, heating units, pumps, conduits etc.) is simulated. Typically, the processing simulation software features computer models which allow process engineers to simulate (and sometimes optimize) the operation of various pieces of equipment used in a proposed or existing manufacturing process. The end results from the simulation software system provide a showing of the simulated (and possibly optimized) performance of the plant under various conditions and estimate of the capital and operating cost of the plant and its profitability.
Generally, simulation and optimization of a process plant model is carried out by one of two fundamentally different approaches:
1. Sequential modular simulation of the plant model, with an optimization algorithm ("optimization block") adjusting the optimization variables after each converged simulation of the complete plant model. PA1 2. Simultaneous solution of the entire plant model, which solves the plant model and optimizes its conditions at the same time. PA1 1. Sequential modular method simulates one process unit at a time, thereby maximizing the utility of the available computing hardware. PA1 2. Highly specialized solution algorithms can be applied to the simulation of a specific process unit model, facilitating convergence of some very difficult models. PA1 3. Modular structure of the software has endorsed a paradigm where a user works with one process unit at a time, specifying operating conditions or product quality. PA1 1. Plant model is essentially serial in structure, i.e. PA1 2. Model specifications do not force repetitive execution of the large sections of the model. PA1 internal convergence loops in the unit operation models having only a final tolerance; often, the convergence loops within different units have different tolerances. PA1 recycle loops, energy integration loops, design specifications all converging with individual tolerances, which are often different from each other. PA1 derivatives evaluated numerically from the models with the internal convergence loops are inaccurate, PA1 excessive execution times are required for evaluation of derivatives in the flowsheets with "upstream" specification or nested recycle loops. PA1 for a given feed and operating conditions, predict the plant product; PA1 for a given set of product and the operating conditions, predict the feed which is entering the plant; PA1 for a given set of product and the feed, estimate the operating parameters of the plant; PA1 minimize simultaneously deviations between model predicted variables and the plant measurements (data reconciliation and parameter estimation). PA1 either variables which are internal to the process units (i.e., reactor wall temperatures, tray liquid loading), or PA1 on the stream measured variables (e.g., %ethane in an ethylene stream), or PA1 on the model parameters (e.g., heat transfer coefficient in an exchanger). PA1 reconcile plant data, PA1 estimate plant parameters, PA1 simulate plant operation, PA1 optimize plant operation. PA1 a) solves the initial plant model through sequential modular simulation. This generates an initial point. and PA1 b) generates an equation oriented plant model which is initialized from the solution in a). This equation oriented model is then used for data reconciliation, parameter estimation, optimization, and simulation.